import Levenshtein
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import shap
import warnings
import xgboost



from pandarallel import pandarallel
from scipy.stats import spearmanr

# Configuration
pandarallel.initialize(use_memory_fs=False)
warnings.filterwarnings('ignore')

INFO: Pandarallel will run on 6 workers.
INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.

Read data

raw_data_path = '../data/raw/'

# You can get this data from: 
#https://www.kaggle.com/datasets/alejopaullier/aminoacids-physical-and-chemical-properties

aminoacids = pd.read_csv(f'{raw_data_path}aminoacids.csv')
aminoacids.rename(
    columns={
        col: col.lower().strip().replace(' ', '_') 
        for col in aminoacids.columns
    }, inplace=True
)

protein_lists_pbd = pd.read_csv(
    f'{raw_data_path}similar_protein_sequences.csv', 
    index_col="seq_id"
)

df_train_updates = pd.read_csv(f"{raw_data_path}train_updates_20220929.csv", index_col="seq_id")
df_train = pd.read_csv(f"{raw_data_path}train.csv", index_col="seq_id")
all_features_nan = df_train_updates.isnull().all("columns")
drop_indices = df_train_updates[all_features_nan].index
df_train = df_train.drop(index=drop_indices)
swap_ph_tm_indices = df_train_updates[~all_features_nan].index
df_train.loc[swap_ph_tm_indices, ["pH", "tm"]] = df_train_updates.loc[swap_ph_tm_indices, ["pH", "tm"]]
df_train = df_train[df_train.protein_sequence.notna()]
df_train = df_train[df_train.pH.notna()]

df_train.head()

	protein_sequence	pH	data_source	tm
seq_id
0	AAAAKAAALALLGEAPEVVDIWLPAGWRQPFRVFRLERKGDGVLVG...	7.0	doi.org/10.1038/s41592-020-0801-4	75.7
1	AAADGEPLHNEEERAGAGQVGRSLPQESEEQRTGSRPRRRRDLGSR...	7.0	doi.org/10.1038/s41592-020-0801-4	50.5
2	AAAFSTPRATSYRILSSAGSGSTRADAPQVRRLHTTRDLLAKDYYA...	7.0	doi.org/10.1038/s41592-020-0801-4	40.5
3	AAASGLRTAIPAQPLRHLLQPAPRPCLRPFGLLSVRAGSARRSGLL...	7.0	doi.org/10.1038/s41592-020-0801-4	47.2
4	AAATKSGPRRQSQGASVRTFTPFYFLVEPVDTLSVRGSSVILNCSA...	7.0	doi.org/10.1038/s41592-020-0801-4	49.5

Process data

def similarity_levenshtein(protein1, protein2):
    distance = Levenshtein.distance(protein1, protein2)
    return distance

def change_positions(protein1, protein2):
    edit_operations = Levenshtein.editops(protein1, protein2)
    result_positions = [element[1] for element in edit_operations]
    return result_positions

def get_protein_similarity_groups(protein_sequence):
    lenght_protein = len(protein_sequence)
    upper_val = lenght_protein + 50
    lower_val = lenght_protein - 50

    filtered_df_train = df_train[
        df_train.protein_len.between(
            lower_val, upper_val
        )
    ]
    protein_seq_similarity_levenshtein = filtered_df_train.protein_sequence.apply(
        lambda protein_sequence2: similarity_levenshtein(
            protein_sequence, protein_sequence2
        )
    )
    levenshtein_list = protein_seq_similarity_levenshtein[
        protein_seq_similarity_levenshtein < 10 # Why 10? 
    ].index.to_list()    
    return levenshtein_list

df_train['protein_len'] = df_train.protein_sequence.str.len()

group_lists = df_train.protein_sequence.parallel_apply(
    get_protein_similarity_groups
)

group_lists_df = pd.DataFrame(group_lists).rename(columns={'protein_sequence': 'group_list'})
group_lists_df['group_list_len'] = group_lists_df.group_list.apply(len)
group_lists_df['group_list_str'] = group_lists_df.group_list.apply(str)
group_lists_df = group_lists_df[group_lists_df.group_list_len > 4]
relevant_groups = {
    gr:f'{i}_group'
    for i, gr in 
    enumerate(
        group_lists_df.group_list_str.unique()
    )
}
group_lists_df['group_str'] = group_lists_df.group_list_str.map(relevant_groups)
df_train.loc[group_lists_df.index, 'group'] = group_lists_df.group_str
reduced_df_train = df_train[df_train.group.notna()]
reduced_df_train['pdb_list'] = protein_lists_pbd.loc[reduced_df_train.index].protein_sequence

def get_paired_mutations(tmp):
    x2 = tmp.protein_sequence.to_list()
    zero_matrix = np.zeros((len(tmp), len(tmp)))
    single_distances_pairs = []
    new_rows = []
    for i, word in enumerate(x2):
        for j, word2 in enumerate(x2):
            distance = similarity_levenshtein(word, word2)
            zero_matrix[i,j] = distance
            if 0 < distance <= 1:
                if i<j:
                    continue
                else:
                    single_distances_pairs.append((i,j))
                    row1 = tmp.iloc[i]
                    row2 = tmp.iloc[j]
                    dd = {}
                    indexes_of_change = change_positions(word, word2)
                    wildtype = word[indexes_of_change[0]]
                    mutation = word2[indexes_of_change[0]]
                    dd['seq1'] = row1.protein_sequence
                    dd['tm1'] = row1.tm
                    dd['ds1'] = row1.data_source
                    dd['pH1'] = row1.pH
                    dd['len1'] = row1.protein_len
                    dd['list1'] = row1.pdb_list
                    dd['seq2'] = row2.protein_sequence
                    dd['tm2'] = row2.tm
                    dd['ds2'] = row2.data_source
                    dd['pH2'] = row2.pH
                    dd['len2'] = row2.protein_len
                    dd['list2'] = row2.pdb_list
                    dd['mutation_type'] = distance
                    dd['mutation'] = mutation
                    dd['wildtype'] = wildtype
                    dd['edit_operations'] = indexes_of_change
                    new_rows.append(dd)
    new_df = pd.DataFrame(new_rows)
    return new_df

paired_df = reduced_df_train.groupby('group').apply(
    get_paired_mutations)

temporal_data = paired_df.copy().reset_index()
temporal_data = temporal_data.drop(
    ['level_1','ds1', 'ds2', 'len2'],
    axis=1
)

temporal_data['mutation_position'] =  temporal_data['edit_operations'].apply(lambda x: x[0])
temporal_data['relative_mutation_position'] = temporal_data['mutation_position']/temporal_data['len1']
temporal_data['d_pH'] = temporal_data['pH2'] - temporal_data['pH1']
temporal_data['d_tm'] = temporal_data['tm2'] - temporal_data['tm1'] 

def aminoacid_data(row):
    letter_wildtype = row.wildtype[0]
    letter_mutation = row.mutation[0]
    wild_amino = aminoacids[aminoacids.letter == letter_wildtype]
    muta_amino = aminoacids[aminoacids.letter == letter_mutation]
    cols = [
        'molecular_weight',
        'residue_weight',
        'pka1',
        'pkb2',
        'pkx3',
        'pl4',
        'h',
        'vsc',
        'p1',
        'p2',
        'sasa',
        'ncisc',
        'carbon',
        'hydrogen',
        'nitrogen',
        'oxygen',
        'sulfur'
    ]
    difference = (
        muta_amino[cols].reset_index(drop=True).T - 
        wild_amino[cols].reset_index(drop=True).T
    )
    result = dict(difference[0])
    result = {f'{key}__difference': result[key] for key in result.keys()}
    return result

temporal_data_aminoacid_info = pd.json_normalize(
    temporal_data.parallel_apply(
        aminoacid_data, axis=1
    )
)

temporal_data_aminoacid_info.head()

	molecular_weight__difference	residue_weight__difference	pka1__difference	pkb2__difference	pkx3__difference	pl4__difference	h__difference	vsc__difference	p1__difference	p2__difference	sasa__difference	ncisc__difference	carbon__difference	hydrogen__difference	nitrogen__difference	oxygen__difference	sulfur__difference
0	-42.08	-42.08	-0.02	0.09	NaN	0.02	-0.44	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0	0.0
1	-30.02	-30.03	0.25	0.59	NaN	0.40	0.67	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0	0.0
2	-57.05	-57.05	0.17	0.56	NaN	0.35	1.47	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0	0.0
3	14.03	14.03	0.00	0.09	NaN	0.03	0.14	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0
4	14.03	14.03	0.00	0.09	NaN	0.03	0.14	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0

drop_cols = [
    'list1', 
#    'len1',
    'tm2',
    'list2',
    'mutation', 
    'wildtype', 
    'edit_operations',
    'mutation_position',
]

final_train_data = pd.concat([
    temporal_data.drop(drop_cols, axis=1),
    temporal_data_aminoacid_info], axis=1)
final_train_data

	group	seq1	tm1	pH1	len1	seq2	pH2	mutation_type	relative_mutation_position	d_pH	...	vsc__difference	p1__difference	p2__difference	sasa__difference	ncisc__difference	carbon__difference	hydrogen__difference	nitrogen__difference	oxygen__difference	sulfur__difference
0	0_group	MKGMSKMLQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	67.3	7.5	53.0	MKGMSKMAQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.132075	0.0	...	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0	0.0
1	0_group	MKGMSKMPQFNLRWPREVLDLVRTVAEENGRSVNSEIYQRVMESFK...	44.0	7.5	53.0	MKGMSKMPQFNLRWPREVLDLVRAVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.433962	0.0	...	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0	0.0
2	0_group	MKGMSKMPQFNLRWPREVQDLVRKVAEENGRSVNSEIYQRVMESFK...	35.0	7.5	53.0	MKGMSKMPQFNLRWPREVADLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.339623	0.0	...	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0	0.0
3	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0
4	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	27.5	-0.9	0.046	0.300	-0.171865	1.0	2.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11665	93_group	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	52.3	7.5	458.0	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	7.5	1.0	0.788210	0.0	...	-66.0	3.2	-0.140	-0.750	-0.044485	-3.0	-6.0	0.0	0.0	0.0
11666	93_group	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	48.6	7.5	458.0	MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRF...	7.5	1.0	0.340611	0.0	...	29.4	1.9	0.072	0.407	0.045859	1.0	1.0	1.0	0.0	0.0
11667	94_group	QDWLTFQKKHITNTRDVDCDNIMSTNLTHCKDKNTFIYSRPEPVKA...	77.4	5.5	104.0	QDWLTFQKKHITNTRDVDCDNIMSTNLAHCKDKNTFIYSRPEPVKA...	5.5	1.0	0.259615	0.0	...	-23.8	-0.5	-0.062	-0.344	0.003835	-1.0	-2.0	0.0	-1.0	0.0
11668	95_group	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	59.2	2.5	68.0	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	2.5	1.0	0.720588	0.0	...	-72.5	-3.2	-0.173	-1.077	-0.010521	-3.0	-7.0	-1.0	0.0	0.0
11669	95_group	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	67.5	2.5	68.0	SSCPGKSSWPHLVGVGGSVAKAIIERQNPNVKAVILEEGTPVTKDF...	2.5	1.0	0.750000	0.0	...	-53.2	-2.4	-0.134	-0.751	-0.042024	-2.0	-3.0	-1.0	-1.0	0.0

11670 rows × 28 columns

def generate_graph(protein_sequence):
    from_to_dict = {
        'from':list(protein_sequence[:-1]),
        'to':list(protein_sequence[1:])
    }
    protein_sequence_directions = pd.DataFrame(from_to_dict)
    list_of_paths = protein_sequence_directions.groupby('from').to.agg(list).values.tolist()
    G=nx.Graph()
    for path in list_of_paths:
        nx.add_path(G, path)
    return G
    
def flatten_result_dict(result_dictionary):
    index = []
    values = []
    for key in result_dictionary.keys():
        for letter in result_dictionary[key].keys():
            index.append(f'{key}_{letter}')
            values.append(result_dictionary[key][letter])
    result = pd.Series(values, index=index)
    return result 

def get_graph_results(graph):
    result_dictionary = {
        'degree_dict':dict(graph.degree()),
        'pagerank_dict': nx.pagerank(graph),
        #'centrality_eigenvector': nx.eigenvector_centrality(graph),
        'centrality_degree': nx.degree_centrality(graph),
        'centrality_closeness': nx.closeness_centrality(graph),
    }
    result_dictionary_df = flatten_result_dict(result_dictionary=result_dictionary)
    return result_dictionary_df

graph_seq1 = final_train_data['seq1'].apply(generate_graph).apply(get_graph_results)
graph_seq2 = final_train_data['seq2'].apply(generate_graph).apply(get_graph_results)

for col in graph_seq1.columns:
    final_train_data[f'{col}_graph_diff'] = (
        graph_seq2[col]-graph_seq1[col]
    )

final_train_data.head()

	group	seq1	tm1	pH1	len1	seq2	pH2	mutation_type	relative_mutation_position	d_pH	...	centrality_degree_T_graph_diff	centrality_closeness_T_graph_diff	degree_dict_C_graph_diff	degree_dict_H_graph_diff	pagerank_dict_C_graph_diff	pagerank_dict_H_graph_diff	centrality_degree_C_graph_diff	centrality_degree_H_graph_diff	centrality_closeness_C_graph_diff	centrality_closeness_H_graph_diff
0	0_group	MKGMSKMLQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	67.3	7.5	53.0	MKGMSKMAQFNLRWPREVLDLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.132075	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0_group	MKGMSKMPQFNLRWPREVLDLVRTVAEENGRSVNSEIYQRVMESFK...	44.0	7.5	53.0	MKGMSKMPQFNLRWPREVLDLVRAVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.433962	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	0_group	MKGMSKMPQFNLRWPREVQDLVRKVAEENGRSVNSEIYQRVMESFK...	35.0	7.5	53.0	MKGMSKMPQFNLRWPREVADLVRKVAEENGRSVNSEIYQRVMESFK...	7.5	1.0	0.339623	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	0.0	0.0	0.0	0.0	0.000667	0.000124	0.0	0.0	0.0	0.0
4	10_group	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	40.0	5.0	150.0	MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAA...	5.0	1.0	0.526667	0.0	...	0.0	0.0	0.0	0.0	0.000667	0.000124	0.0	0.0	0.0	0.0

5 rows × 108 columns

def aminoacid_window_letter_sum_func(df):
    position = df.relative_mutation_position * len(df.seq1)
    left_position = position - 2
    right_position = position + 2
    aminoacid_window = df.seq1[int(left_position):int(right_position + 1)]
    list_test = []
    for i, letter in enumerate(aminoacid_window):
        if i==2:
            continue
        list_test.append(aminoacids[aminoacids.letter==letter])
    if not list_test:
        return None
    concatenated_letters = pd.concat(list_test)
    total_vecinity = concatenated_letters.select_dtypes('number').sum()
    rival_vecinity = concatenated_letters.select_dtypes('number').head(2).sum()-concatenated_letters.select_dtypes('number').tail(2).sum()
    total_vecinity.rename(index={ix:f'total_sum_neighbour_{ix}' for ix in total_vecinity.index}, inplace=True)
    rival_vecinity.rename(index={ix:f'rival_dif_neighbour_{ix}' for ix in rival_vecinity.index}, inplace=True)
    vecinity_results = total_vecinity.append(rival_vecinity)
    return vecinity_results

vecinity_results = final_train_data.apply(
    aminoacid_window_letter_sum_func, axis=1
)

vecinity_results

	total_sum_neighbour_molecular_weight	total_sum_neighbour_residue_weight	total_sum_neighbour_pka1	total_sum_neighbour_pkb2	total_sum_neighbour_pkx3	total_sum_neighbour_pl4	total_sum_neighbour_h	total_sum_neighbour_vsc	total_sum_neighbour_p1	total_sum_neighbour_p2	...	rival_dif_neighbour_vsc	rival_dif_neighbour_p1	rival_dif_neighbour_p2	rival_dif_neighbour_sasa	rival_dif_neighbour_ncisc	rival_dif_neighbour_carbon	rival_dif_neighbour_hydrogen	rival_dif_neighbour_nitrogen	rival_dif_neighbour_oxygen	rival_dif_neighbour_sulfur
0	606.74	534.69	8.46	36.42	10.53	26.61	-0.52	390.3	32.7	0.910	...	-2.1	1.3	-3.000000e-02	0.132	-0.066372	-3.0	4.0	0.0	-1.0	1.0
1	497.60	425.53	9.15	37.97	12.48	28.68	0.25	275.5	30.4	0.617	...	77.5	2.4	2.450000e-01	1.379	0.036400	3.0	7.0	3.0	0.0	0.0
2	528.57	456.50	8.75	38.49	7.90	17.93	0.50	267.0	36.1	0.582	...	0.0	0.3	5.551115e-17	-0.011	0.035954	0.0	0.0	0.0	0.0	0.0
3	441.50	369.42	9.21	38.02	0.00	23.60	1.31	201.7	32.5	0.412	...	40.3	-6.5	5.200000e-02	0.299	-0.169404	2.0	5.0	-1.0	-1.0	0.0
4	441.50	369.42	9.21	38.02	0.00	23.60	1.31	201.7	32.5	0.412	...	40.3	-6.5	5.200000e-02	0.299	-0.169404	2.0	5.0	-1.0	-1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11665	489.55	417.50	8.55	37.83	8.18	22.18	0.71	226.1	31.5	0.510	...	-78.3	-2.1	-1.300000e-01	-0.706	-0.059006	-4.0	-7.0	-1.0	0.0	1.0
11666	467.53	395.47	8.71	38.69	0.00	23.73	1.34	221.6	32.9	0.497	...	20.4	-6.3	-3.300000e-02	-0.132	-0.216105	0.0	3.0	-1.0	-1.0	0.0
11667	539.62	467.56	8.16	37.85	14.18	24.05	0.17	275.8	32.4	0.678	...	28.6	0.6	-3.800000e-02	0.100	0.104364	1.0	5.0	-1.0	1.0	-1.0
11668	544.63	472.58	8.47	37.74	20.66	27.20	-1.94	279.8	33.5	0.693	...	-73.2	0.7	-1.690000e-01	-1.089	-0.131809	-4.0	-10.0	-2.0	1.0	1.0
11669	626.76	554.70	9.68	37.65	12.48	28.63	0.74	415.5	27.0	1.026	...	-62.5	5.8	-1.640000e-01	-0.268	0.040983	-6.0	0.0	2.0	0.0	0.0

11670 rows × 34 columns

final_train_data = pd.concat([final_train_data, vecinity_results], axis=1)

for i in range(3):
    final_train_data[f'random_{i}'] = np.random.randint(0, 1000, len(final_train_data))

Train-test splits

unique_groups = final_train_data.group.unique()
test_groups = np.random.choice(
    unique_groups, int(len(unique_groups)*0.3)
)
remaining_groups = list(set(unique_groups) - set(test_groups))
dev_groups = np.random.choice(
    remaining_groups, int(len(unique_groups)*0.2)
)

train_groups = list(set(remaining_groups) - set(dev_groups))

test_data = final_train_data[final_train_data.group.isin(test_groups)]
dev_data = final_train_data[final_train_data.group.isin(dev_groups)]
train_data = final_train_data[final_train_data.group.isin(train_groups)]

non_train_data = ['group', 'd_tm', 'seq1', 'seq2', 'tm1']
X_train, y_train = train_data.drop(non_train_data, axis=1), train_data.d_tm
X_test, y_test = test_data.drop(non_train_data, axis=1), test_data.d_tm
X_dev, y_dev = test_data.drop(non_train_data, axis=1), test_data.d_tm

Train model

model = xgboost.XGBRegressor(
    n_estimators=100000,
    max_depth=6,
    learning_rate=0.005,
    subsample=0.8,
    colsample_bytree=0.8,
)

model.fit(
    X_train, 
    y_train, 
    eval_set=[(X_train, y_train), (X_dev, y_dev)],
    early_stopping_rounds=1000,
    verbose=100
)

[0]	validation_0-rmse:10.59527	validation_1-rmse:12.14599
[100]	validation_0-rmse:9.55465	validation_1-rmse:11.54001
[200]	validation_0-rmse:8.94047	validation_1-rmse:11.21056
[300]	validation_0-rmse:8.54372	validation_1-rmse:10.99881
[400]	validation_0-rmse:8.21935	validation_1-rmse:10.87808
[500]	validation_0-rmse:7.95642	validation_1-rmse:10.83498
[600]	validation_0-rmse:7.72807	validation_1-rmse:10.81281
[700]	validation_0-rmse:7.54389	validation_1-rmse:10.80298
[800]	validation_0-rmse:7.35820	validation_1-rmse:10.80165
[900]	validation_0-rmse:7.19387	validation_1-rmse:10.81396
[1000]	validation_0-rmse:7.03416	validation_1-rmse:10.79945
[1100]	validation_0-rmse:6.87993	validation_1-rmse:10.77744
[1200]	validation_0-rmse:6.74422	validation_1-rmse:10.77428
[1300]	validation_0-rmse:6.60023	validation_1-rmse:10.75972
[1400]	validation_0-rmse:6.47327	validation_1-rmse:10.76491
[1500]	validation_0-rmse:6.34158	validation_1-rmse:10.77655
[1600]	validation_0-rmse:6.22003	validation_1-rmse:10.77505
[1700]	validation_0-rmse:6.09990	validation_1-rmse:10.77965
[1800]	validation_0-rmse:5.98274	validation_1-rmse:10.78624
[1900]	validation_0-rmse:5.88094	validation_1-rmse:10.78953
[2000]	validation_0-rmse:5.77285	validation_1-rmse:10.80853
[2100]	validation_0-rmse:5.65813	validation_1-rmse:10.80955
[2200]	validation_0-rmse:5.55588	validation_1-rmse:10.81516
[2300]	validation_0-rmse:5.45584	validation_1-rmse:10.81926
[2368]	validation_0-rmse:5.38837	validation_1-rmse:10.83124

XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.8,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
             grow_policy='depthwise', importance_type=None,
             interaction_constraints='', learning_rate=0.005, max_bin=256,
             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,
             monotone_constraints='()', n_estimators=100000, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, ...)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

plt.scatter(
    y_test,
    model.predict(X_test),
    s=0.1
)
plt.plot([-40,40], [-40,40])

[<matplotlib.lines.Line2D at 0x7ffaf93267c0>]

spearmanr(
    y_test,
    model.predict(X_test)
)

SpearmanrResult(correlation=0.40507926191715465, pvalue=3.2775376856156347e-274)

feature_importances_df = pd.Series(
    model.feature_importances_, index=X_test.columns
).sort_values()

plt.figure(figsize=(10,30))
plt.barh(feature_importances_df.index,feature_importances_df)

<BarContainer object of 140 artists>

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

shap.summary_plot(shap_values, X_test)

top_inds = np.argsort(-np.sum(np.abs(shap_values), 0))

for i in range(20):
    shap.dependence_plot(top_inds[i], shap_values, X_test)

model = xgboost.XGBClassifier(
    n_estimators=100000,
    max_depth=4,
    learning_rate=0.001,
    subsample=0.6,
    colsample_bytree=0.2,
)

model.fit(
    X_train, 
    y_train > 0, 
    eval_set=[(X_train, y_train > 0), (X_dev, y_dev > 0)],
    eval_metric=['auc', 'logloss'],
    early_stopping_rounds=1000,
)

[0]	validation_0-auc:0.66316	validation_0-logloss:0.69303	validation_1-auc:0.50717	validation_1-logloss:0.69312
[1]	validation_0-auc:0.71048	validation_0-logloss:0.69291	validation_1-auc:0.58082	validation_1-logloss:0.69304
[2]	validation_0-auc:0.72107	validation_0-logloss:0.69278	validation_1-auc:0.57919	validation_1-logloss:0.69298
[3]	validation_0-auc:0.72567	validation_0-logloss:0.69269	validation_1-auc:0.57909	validation_1-logloss:0.69297
[4]	validation_0-auc:0.73257	validation_0-logloss:0.69258	validation_1-auc:0.61402	validation_1-logloss:0.69289
[5]	validation_0-auc:0.73858	validation_0-logloss:0.69247	validation_1-auc:0.61808	validation_1-logloss:0.69283
[6]	validation_0-auc:0.74558	validation_0-logloss:0.69235	validation_1-auc:0.62422	validation_1-logloss:0.69278
[7]	validation_0-auc:0.75309	validation_0-logloss:0.69222	validation_1-auc:0.62226	validation_1-logloss:0.69273
[8]	validation_0-auc:0.75609	validation_0-logloss:0.69208	validation_1-auc:0.61945	validation_1-logloss:0.69269
[9]	validation_0-auc:0.75774	validation_0-logloss:0.69197	validation_1-auc:0.62272	validation_1-logloss:0.69262
[10]	validation_0-auc:0.75912	validation_0-logloss:0.69184	validation_1-auc:0.61977	validation_1-logloss:0.69257
[11]	validation_0-auc:0.75847	validation_0-logloss:0.69173	validation_1-auc:0.61833	validation_1-logloss:0.69252
[12]	validation_0-auc:0.75892	validation_0-logloss:0.69162	validation_1-auc:0.61617	validation_1-logloss:0.69248
[13]	validation_0-auc:0.75637	validation_0-logloss:0.69154	validation_1-auc:0.61475	validation_1-logloss:0.69244
[14]	validation_0-auc:0.75750	validation_0-logloss:0.69142	validation_1-auc:0.61369	validation_1-logloss:0.69243
[15]	validation_0-auc:0.75778	validation_0-logloss:0.69128	validation_1-auc:0.61310	validation_1-logloss:0.69238
[16]	validation_0-auc:0.75798	validation_0-logloss:0.69115	validation_1-auc:0.61780	validation_1-logloss:0.69233
[17]	validation_0-auc:0.75852	validation_0-logloss:0.69103	validation_1-auc:0.61309	validation_1-logloss:0.69230
[18]	validation_0-auc:0.75840	validation_0-logloss:0.69093	validation_1-auc:0.60997	validation_1-logloss:0.69228
[19]	validation_0-auc:0.75832	validation_0-logloss:0.69081	validation_1-auc:0.60801	validation_1-logloss:0.69224
[20]	validation_0-auc:0.75761	validation_0-logloss:0.69068	validation_1-auc:0.60865	validation_1-logloss:0.69218
[21]	validation_0-auc:0.75855	validation_0-logloss:0.69060	validation_1-auc:0.60640	validation_1-logloss:0.69216
[22]	validation_0-auc:0.75962	validation_0-logloss:0.69049	validation_1-auc:0.60771	validation_1-logloss:0.69215
[23]	validation_0-auc:0.75868	validation_0-logloss:0.69040	validation_1-auc:0.60559	validation_1-logloss:0.69211
[24]	validation_0-auc:0.75811	validation_0-logloss:0.69029	validation_1-auc:0.60769	validation_1-logloss:0.69206
[25]	validation_0-auc:0.75895	validation_0-logloss:0.69020	validation_1-auc:0.60787	validation_1-logloss:0.69203
[26]	validation_0-auc:0.76034	validation_0-logloss:0.69011	validation_1-auc:0.60611	validation_1-logloss:0.69201
[27]	validation_0-auc:0.76059	validation_0-logloss:0.69003	validation_1-auc:0.60765	validation_1-logloss:0.69198
[28]	validation_0-auc:0.76018	validation_0-logloss:0.68995	validation_1-auc:0.60592	validation_1-logloss:0.69196
[29]	validation_0-auc:0.76037	validation_0-logloss:0.68984	validation_1-auc:0.60585	validation_1-logloss:0.69193
[30]	validation_0-auc:0.76005	validation_0-logloss:0.68972	validation_1-auc:0.60462	validation_1-logloss:0.69190
[31]	validation_0-auc:0.76067	validation_0-logloss:0.68963	validation_1-auc:0.60695	validation_1-logloss:0.69186
[32]	validation_0-auc:0.76053	validation_0-logloss:0.68951	validation_1-auc:0.60929	validation_1-logloss:0.69178
[33]	validation_0-auc:0.76190	validation_0-logloss:0.68937	validation_1-auc:0.61522	validation_1-logloss:0.69169
[34]	validation_0-auc:0.76268	validation_0-logloss:0.68926	validation_1-auc:0.61329	validation_1-logloss:0.69167
[35]	validation_0-auc:0.76249	validation_0-logloss:0.68913	validation_1-auc:0.61265	validation_1-logloss:0.69163
[36]	validation_0-auc:0.76289	validation_0-logloss:0.68900	validation_1-auc:0.61120	validation_1-logloss:0.69159
[37]	validation_0-auc:0.76244	validation_0-logloss:0.68888	validation_1-auc:0.61097	validation_1-logloss:0.69155
[38]	validation_0-auc:0.76239	validation_0-logloss:0.68877	validation_1-auc:0.61159	validation_1-logloss:0.69149
[39]	validation_0-auc:0.76250	validation_0-logloss:0.68867	validation_1-auc:0.61100	validation_1-logloss:0.69144
[40]	validation_0-auc:0.76281	validation_0-logloss:0.68853	validation_1-auc:0.61122	validation_1-logloss:0.69140
[41]	validation_0-auc:0.76217	validation_0-logloss:0.68844	validation_1-auc:0.61153	validation_1-logloss:0.69136
[42]	validation_0-auc:0.76137	validation_0-logloss:0.68833	validation_1-auc:0.61092	validation_1-logloss:0.69131
[43]	validation_0-auc:0.76112	validation_0-logloss:0.68820	validation_1-auc:0.61164	validation_1-logloss:0.69126
[44]	validation_0-auc:0.76152	validation_0-logloss:0.68807	validation_1-auc:0.61318	validation_1-logloss:0.69119
[45]	validation_0-auc:0.76163	validation_0-logloss:0.68799	validation_1-auc:0.61378	validation_1-logloss:0.69116
[46]	validation_0-auc:0.76141	validation_0-logloss:0.68791	validation_1-auc:0.61297	validation_1-logloss:0.69114
[47]	validation_0-auc:0.76177	validation_0-logloss:0.68783	validation_1-auc:0.61266	validation_1-logloss:0.69112
[48]	validation_0-auc:0.76189	validation_0-logloss:0.68769	validation_1-auc:0.61523	validation_1-logloss:0.69103
[49]	validation_0-auc:0.76132	validation_0-logloss:0.68757	validation_1-auc:0.61444	validation_1-logloss:0.69098
[50]	validation_0-auc:0.76118	validation_0-logloss:0.68748	validation_1-auc:0.61333	validation_1-logloss:0.69096
[51]	validation_0-auc:0.76162	validation_0-logloss:0.68736	validation_1-auc:0.61218	validation_1-logloss:0.69093
[52]	validation_0-auc:0.76152	validation_0-logloss:0.68724	validation_1-auc:0.61073	validation_1-logloss:0.69090
[53]	validation_0-auc:0.76195	validation_0-logloss:0.68710	validation_1-auc:0.60973	validation_1-logloss:0.69086
[54]	validation_0-auc:0.76170	validation_0-logloss:0.68698	validation_1-auc:0.60959	validation_1-logloss:0.69081
[55]	validation_0-auc:0.76140	validation_0-logloss:0.68688	validation_1-auc:0.60998	validation_1-logloss:0.69076
[56]	validation_0-auc:0.76142	validation_0-logloss:0.68676	validation_1-auc:0.61236	validation_1-logloss:0.69069
[57]	validation_0-auc:0.76159	validation_0-logloss:0.68667	validation_1-auc:0.61215	validation_1-logloss:0.69067
[58]	validation_0-auc:0.76209	validation_0-logloss:0.68655	validation_1-auc:0.61138	validation_1-logloss:0.69063
[59]	validation_0-auc:0.76224	validation_0-logloss:0.68643	validation_1-auc:0.61150	validation_1-logloss:0.69058
[60]	validation_0-auc:0.76239	validation_0-logloss:0.68631	validation_1-auc:0.61124	validation_1-logloss:0.69053
[61]	validation_0-auc:0.76280	validation_0-logloss:0.68621	validation_1-auc:0.61021	validation_1-logloss:0.69054
[62]	validation_0-auc:0.76272	validation_0-logloss:0.68608	validation_1-auc:0.61024	validation_1-logloss:0.69050
[63]	validation_0-auc:0.76287	validation_0-logloss:0.68596	validation_1-auc:0.61602	validation_1-logloss:0.69038
[64]	validation_0-auc:0.76313	validation_0-logloss:0.68584	validation_1-auc:0.61672	validation_1-logloss:0.69031
[65]	validation_0-auc:0.76329	validation_0-logloss:0.68575	validation_1-auc:0.61705	validation_1-logloss:0.69028
[66]	validation_0-auc:0.76306	validation_0-logloss:0.68563	validation_1-auc:0.61631	validation_1-logloss:0.69024
[67]	validation_0-auc:0.76325	validation_0-logloss:0.68550	validation_1-auc:0.61518	validation_1-logloss:0.69021
[68]	validation_0-auc:0.76357	validation_0-logloss:0.68541	validation_1-auc:0.61448	validation_1-logloss:0.69020
[69]	validation_0-auc:0.76357	validation_0-logloss:0.68530	validation_1-auc:0.61369	validation_1-logloss:0.69017
[70]	validation_0-auc:0.76353	validation_0-logloss:0.68518	validation_1-auc:0.61350	validation_1-logloss:0.69013
[71]	validation_0-auc:0.76359	validation_0-logloss:0.68507	validation_1-auc:0.61554	validation_1-logloss:0.69007
[72]	validation_0-auc:0.76394	validation_0-logloss:0.68494	validation_1-auc:0.61503	validation_1-logloss:0.69004
[73]	validation_0-auc:0.76374	validation_0-logloss:0.68483	validation_1-auc:0.61562	validation_1-logloss:0.68997
[74]	validation_0-auc:0.76350	validation_0-logloss:0.68474	validation_1-auc:0.61551	validation_1-logloss:0.68992
[75]	validation_0-auc:0.76396	validation_0-logloss:0.68463	validation_1-auc:0.62098	validation_1-logloss:0.68979
[76]	validation_0-auc:0.76389	validation_0-logloss:0.68453	validation_1-auc:0.62133	validation_1-logloss:0.68974
[77]	validation_0-auc:0.76391	validation_0-logloss:0.68442	validation_1-auc:0.61999	validation_1-logloss:0.68972
[78]	validation_0-auc:0.76371	validation_0-logloss:0.68430	validation_1-auc:0.61913	validation_1-logloss:0.68968
[79]	validation_0-auc:0.76429	validation_0-logloss:0.68417	validation_1-auc:0.61804	validation_1-logloss:0.68965
[80]	validation_0-auc:0.76463	validation_0-logloss:0.68407	validation_1-auc:0.62018	validation_1-logloss:0.68957
[81]	validation_0-auc:0.76482	validation_0-logloss:0.68400	validation_1-auc:0.61991	validation_1-logloss:0.68955
[82]	validation_0-auc:0.76547	validation_0-logloss:0.68389	validation_1-auc:0.61948	validation_1-logloss:0.68953
[83]	validation_0-auc:0.76541	validation_0-logloss:0.68380	validation_1-auc:0.61937	validation_1-logloss:0.68950
[84]	validation_0-auc:0.76567	validation_0-logloss:0.68369	validation_1-auc:0.61858	validation_1-logloss:0.68948
[85]	validation_0-auc:0.76583	validation_0-logloss:0.68361	validation_1-auc:0.61819	validation_1-logloss:0.68946
[86]	validation_0-auc:0.76570	validation_0-logloss:0.68351	validation_1-auc:0.61861	validation_1-logloss:0.68941
[87]	validation_0-auc:0.76565	validation_0-logloss:0.68339	validation_1-auc:0.61839	validation_1-logloss:0.68936
[88]	validation_0-auc:0.76555	validation_0-logloss:0.68326	validation_1-auc:0.61631	validation_1-logloss:0.68935
[89]	validation_0-auc:0.76570	validation_0-logloss:0.68316	validation_1-auc:0.61576	validation_1-logloss:0.68932
[90]	validation_0-auc:0.76611	validation_0-logloss:0.68307	validation_1-auc:0.61493	validation_1-logloss:0.68932
[91]	validation_0-auc:0.76619	validation_0-logloss:0.68294	validation_1-auc:0.61659	validation_1-logloss:0.68924
[92]	validation_0-auc:0.76617	validation_0-logloss:0.68284	validation_1-auc:0.61635	validation_1-logloss:0.68920
[93]	validation_0-auc:0.76631	validation_0-logloss:0.68274	validation_1-auc:0.61633	validation_1-logloss:0.68916
[94]	validation_0-auc:0.76643	validation_0-logloss:0.68264	validation_1-auc:0.61645	validation_1-logloss:0.68912
[95]	validation_0-auc:0.76643	validation_0-logloss:0.68251	validation_1-auc:0.61573	validation_1-logloss:0.68909
[96]	validation_0-auc:0.76628	validation_0-logloss:0.68243	validation_1-auc:0.61547	validation_1-logloss:0.68905
[97]	validation_0-auc:0.76644	validation_0-logloss:0.68231	validation_1-auc:0.61492	validation_1-logloss:0.68902
[98]	validation_0-auc:0.76621	validation_0-logloss:0.68219	validation_1-auc:0.61570	validation_1-logloss:0.68895
[99]	validation_0-auc:0.76607	validation_0-logloss:0.68208	validation_1-auc:0.61677	validation_1-logloss:0.68888
[100]	validation_0-auc:0.76626	validation_0-logloss:0.68195	validation_1-auc:0.61549	validation_1-logloss:0.68886
[101]	validation_0-auc:0.76625	validation_0-logloss:0.68184	validation_1-auc:0.61503	validation_1-logloss:0.68883
[102]	validation_0-auc:0.76621	validation_0-logloss:0.68175	validation_1-auc:0.61513	validation_1-logloss:0.68879
[103]	validation_0-auc:0.76623	validation_0-logloss:0.68164	validation_1-auc:0.61457	validation_1-logloss:0.68876
[104]	validation_0-auc:0.76629	validation_0-logloss:0.68154	validation_1-auc:0.61461	validation_1-logloss:0.68872
[105]	validation_0-auc:0.76644	validation_0-logloss:0.68142	validation_1-auc:0.61391	validation_1-logloss:0.68870
[106]	validation_0-auc:0.76635	validation_0-logloss:0.68130	validation_1-auc:0.61354	validation_1-logloss:0.68866
[107]	validation_0-auc:0.76626	validation_0-logloss:0.68119	validation_1-auc:0.61347	validation_1-logloss:0.68862
[108]	validation_0-auc:0.76654	validation_0-logloss:0.68111	validation_1-auc:0.61345	validation_1-logloss:0.68862
[109]	validation_0-auc:0.76684	validation_0-logloss:0.68103	validation_1-auc:0.61321	validation_1-logloss:0.68859
[110]	validation_0-auc:0.76698	validation_0-logloss:0.68094	validation_1-auc:0.61404	validation_1-logloss:0.68855
[111]	validation_0-auc:0.76737	validation_0-logloss:0.68083	validation_1-auc:0.61392	validation_1-logloss:0.68854
[112]	validation_0-auc:0.76793	validation_0-logloss:0.68075	validation_1-auc:0.61379	validation_1-logloss:0.68854
[113]	validation_0-auc:0.76798	validation_0-logloss:0.68064	validation_1-auc:0.61417	validation_1-logloss:0.68848
[114]	validation_0-auc:0.76809	validation_0-logloss:0.68052	validation_1-auc:0.61416	validation_1-logloss:0.68844
[115]	validation_0-auc:0.76825	validation_0-logloss:0.68042	validation_1-auc:0.61422	validation_1-logloss:0.68840
[116]	validation_0-auc:0.76821	validation_0-logloss:0.68030	validation_1-auc:0.61362	validation_1-logloss:0.68837
[117]	validation_0-auc:0.76811	validation_0-logloss:0.68019	validation_1-auc:0.61328	validation_1-logloss:0.68834
[118]	validation_0-auc:0.76800	validation_0-logloss:0.68011	validation_1-auc:0.61355	validation_1-logloss:0.68829
[119]	validation_0-auc:0.76806	validation_0-logloss:0.68002	validation_1-auc:0.61303	validation_1-logloss:0.68829
[120]	validation_0-auc:0.76804	validation_0-logloss:0.67994	validation_1-auc:0.61240	validation_1-logloss:0.68828
[121]	validation_0-auc:0.76764	validation_0-logloss:0.67984	validation_1-auc:0.61222	validation_1-logloss:0.68823
[122]	validation_0-auc:0.76770	validation_0-logloss:0.67974	validation_1-auc:0.61235	validation_1-logloss:0.68819
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[1995]	validation_0-auc:0.80861	validation_0-logloss:0.58009	validation_1-auc:0.63175	validation_1-logloss:0.66001
[1996]	validation_0-auc:0.80862	validation_0-logloss:0.58006	validation_1-auc:0.63178	validation_1-logloss:0.66000
[1997]	validation_0-auc:0.80865	validation_0-logloss:0.58003	validation_1-auc:0.63179	validation_1-logloss:0.65999
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[2003]	validation_0-auc:0.80869	validation_0-logloss:0.57989	validation_1-auc:0.63182	validation_1-logloss:0.65995
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[2007]	validation_0-auc:0.80878	validation_0-logloss:0.57979	validation_1-auc:0.63181	validation_1-logloss:0.65995
[2008]	validation_0-auc:0.80880	validation_0-logloss:0.57977	validation_1-auc:0.63179	validation_1-logloss:0.65995
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[2014]	validation_0-auc:0.80890	validation_0-logloss:0.57964	validation_1-auc:0.63173	validation_1-logloss:0.65994
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[13098]	validation_0-auc:0.90993	validation_0-logloss:0.44188	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13099]	validation_0-auc:0.90994	validation_0-logloss:0.44187	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13100]	validation_0-auc:0.90995	validation_0-logloss:0.44186	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13101]	validation_0-auc:0.90994	validation_0-logloss:0.44186	validation_1-auc:0.67581	validation_1-logloss:0.63348
[13102]	validation_0-auc:0.90995	validation_0-logloss:0.44185	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13103]	validation_0-auc:0.90996	validation_0-logloss:0.44184	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13104]	validation_0-auc:0.90997	validation_0-logloss:0.44183	validation_1-auc:0.67582	validation_1-logloss:0.63347
[13105]	validation_0-auc:0.90997	validation_0-logloss:0.44183	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13106]	validation_0-auc:0.90997	validation_0-logloss:0.44182	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13107]	validation_0-auc:0.90997	validation_0-logloss:0.44181	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13108]	validation_0-auc:0.90998	validation_0-logloss:0.44180	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13109]	validation_0-auc:0.90999	validation_0-logloss:0.44179	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13110]	validation_0-auc:0.90999	validation_0-logloss:0.44179	validation_1-auc:0.67577	validation_1-logloss:0.63349
[13111]	validation_0-auc:0.91000	validation_0-logloss:0.44178	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13112]	validation_0-auc:0.91000	validation_0-logloss:0.44177	validation_1-auc:0.67576	validation_1-logloss:0.63350
[13113]	validation_0-auc:0.91001	validation_0-logloss:0.44177	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13114]	validation_0-auc:0.91001	validation_0-logloss:0.44175	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13115]	validation_0-auc:0.91002	validation_0-logloss:0.44174	validation_1-auc:0.67578	validation_1-logloss:0.63349
[13116]	validation_0-auc:0.91002	validation_0-logloss:0.44173	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13117]	validation_0-auc:0.91003	validation_0-logloss:0.44173	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13118]	validation_0-auc:0.91003	validation_0-logloss:0.44172	validation_1-auc:0.67579	validation_1-logloss:0.63348
[13119]	validation_0-auc:0.91004	validation_0-logloss:0.44171	validation_1-auc:0.67580	validation_1-logloss:0.63348
[13120]	validation_0-auc:0.91004	validation_0-logloss:0.44171	validation_1-auc:0.67582	validation_1-logloss:0.63346
[13121]	validation_0-auc:0.91005	validation_0-logloss:0.44170	validation_1-auc:0.67583	validation_1-logloss:0.63346
[13122]	validation_0-auc:0.91005	validation_0-logloss:0.44168	validation_1-auc:0.67586	validation_1-logloss:0.63345
[13123]	validation_0-auc:0.91005	validation_0-logloss:0.44168	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13124]	validation_0-auc:0.91006	validation_0-logloss:0.44167	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13125]	validation_0-auc:0.91006	validation_0-logloss:0.44167	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13126]	validation_0-auc:0.91006	validation_0-logloss:0.44166	validation_1-auc:0.67585	validation_1-logloss:0.63345
[13127]	validation_0-auc:0.91008	validation_0-logloss:0.44165	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13128]	validation_0-auc:0.91008	validation_0-logloss:0.44164	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13129]	validation_0-auc:0.91009	validation_0-logloss:0.44163	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13130]	validation_0-auc:0.91009	validation_0-logloss:0.44163	validation_1-auc:0.67588	validation_1-logloss:0.63343
[13131]	validation_0-auc:0.91009	validation_0-logloss:0.44162	validation_1-auc:0.67588	validation_1-logloss:0.63343
[13132]	validation_0-auc:0.91009	validation_0-logloss:0.44162	validation_1-auc:0.67586	validation_1-logloss:0.63344
[13133]	validation_0-auc:0.91010	validation_0-logloss:0.44161	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13134]	validation_0-auc:0.91011	validation_0-logloss:0.44160	validation_1-auc:0.67588	validation_1-logloss:0.63344
[13135]	validation_0-auc:0.91011	validation_0-logloss:0.44160	validation_1-auc:0.67587	validation_1-logloss:0.63344
[13136]	validation_0-auc:0.91011	validation_0-logloss:0.44158	validation_1-auc:0.67596	validation_1-logloss:0.63339
[13137]	validation_0-auc:0.91012	validation_0-logloss:0.44157	validation_1-auc:0.67596	validation_1-logloss:0.63339
[13138]	validation_0-auc:0.91012	validation_0-logloss:0.44156	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13139]	validation_0-auc:0.91012	validation_0-logloss:0.44156	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13140]	validation_0-auc:0.91012	validation_0-logloss:0.44155	validation_1-auc:0.67597	validation_1-logloss:0.63338
[13141]	validation_0-auc:0.91013	validation_0-logloss:0.44155	validation_1-auc:0.67598	validation_1-logloss:0.63338
[13142]	validation_0-auc:0.91013	validation_0-logloss:0.44154	validation_1-auc:0.67598	validation_1-logloss:0.63339
[13143]	validation_0-auc:0.91013	validation_0-logloss:0.44153	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13144]	validation_0-auc:0.91014	validation_0-logloss:0.44152	validation_1-auc:0.67594	validation_1-logloss:0.63340
[13145]	validation_0-auc:0.91015	validation_0-logloss:0.44150	validation_1-auc:0.67598	validation_1-logloss:0.63338
[13146]	validation_0-auc:0.91016	validation_0-logloss:0.44150	validation_1-auc:0.67597	validation_1-logloss:0.63339
[13147]	validation_0-auc:0.91016	validation_0-logloss:0.44149	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13148]	validation_0-auc:0.91016	validation_0-logloss:0.44148	validation_1-auc:0.67600	validation_1-logloss:0.63338
[13149]	validation_0-auc:0.91016	validation_0-logloss:0.44148	validation_1-auc:0.67599	validation_1-logloss:0.63338
[13150]	validation_0-auc:0.91017	validation_0-logloss:0.44147	validation_1-auc:0.67601	validation_1-logloss:0.63337
[13151]	validation_0-auc:0.91018	validation_0-logloss:0.44145	validation_1-auc:0.67589	validation_1-logloss:0.63345
[13152]	validation_0-auc:0.91018	validation_0-logloss:0.44145	validation_1-auc:0.67582	validation_1-logloss:0.63349
[13153]	validation_0-auc:0.91018	validation_0-logloss:0.44144	validation_1-auc:0.67582	validation_1-logloss:0.63348
[13154]	validation_0-auc:0.91018	validation_0-logloss:0.44144	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13155]	validation_0-auc:0.91019	validation_0-logloss:0.44143	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13156]	validation_0-auc:0.91020	validation_0-logloss:0.44143	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13157]	validation_0-auc:0.91020	validation_0-logloss:0.44142	validation_1-auc:0.67583	validation_1-logloss:0.63348
[13158]	validation_0-auc:0.91021	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13159]	validation_0-auc:0.91021	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13160]	validation_0-auc:0.91022	validation_0-logloss:0.44141	validation_1-auc:0.67584	validation_1-logloss:0.63347
[13161]	validation_0-auc:0.91022	validation_0-logloss:0.44140	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13162]	validation_0-auc:0.91022	validation_0-logloss:0.44139	validation_1-auc:0.67583	validation_1-logloss:0.63347
[13163]	validation_0-auc:0.91023	validation_0-logloss:0.44138	validation_1-auc:0.67585	validation_1-logloss:0.63346

XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.2,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,
              grow_policy='depthwise', importance_type=None,
              interaction_constraints='', learning_rate=0.001, max_bin=256,
              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,
              max_depth=4, max_leaves=0, min_child_weight=1, missing=nan,
              monotone_constraints='()', n_estimators=100000, n_jobs=0,
              num_parallel_tree=1, predictor='auto', random_state=0, ...)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

top_inds = np.argsort(-np.sum(np.abs(shap_values), 0))

for i in range(20):
    shap.dependence_plot(top_inds[i], shap_values, X_test)